Of the six most valuable companies in the world, five are tech firms, whose worth is embodied less in physical assets than in intellectual property and customer data. Apple and Microsoft provide the tools to connect people, and to collect and process data. Google, Facebook and Amazon provide services, and in return accumulate more data about each customer than has ever been possible before. In seventh place trails Exxon Mobil, an illustration of the well-worn assertion that data is the new oil to fuel the next industrial revolution. If data is the new oil, what is the equivalent of the internal combustion engine, that turned fossil fuels from a cleaner fluid for oil lamps to a world-changing energy source for cars, tractors and aeroplanes?

Fast-food workers, cashiers, cooks, delivery people and their supporters held a rally outside New York City Hall on May 24, 2017.Erik Mcgregor/Pacific Press/Zuma From the window of his university office in Louvain-la-Neuve, Belgium, philosophy professor Philippe Van Parijs--considered by many to be Europe's most prominent advocate for the idea that the state should provide a regular income to every citizen--can see the mailbox where he sent off invitations to the first "basic income" conference more than 30 years ago. "I'm quite amazed by the seed we threw on the ground now," he says. After decades of obscurity, the idea is suddenly in fashion. Politicians around the world are interested and a handful of governments, such as Finland and the Canadian province of Ontario, are planning or considering basic-income pilot projects. But the idea of basic income has been around for more than 200 years, rising on waves of political and economic turmoil only to disappear in calmer times.

Ma believes that companies must be prepared for decades of pain to come to terms with the advance of robots. This sentiment was reinforced by the president of the World Bank (https://www.weforum. So are all jobs at risk? But the factors impacting jobs will be many (see chart). Countries will have to navigate carefully, to find ways to ensure that job formation efforts do not flag.

From sales funnel acceleration to network management automation, artificial intelligence (AI) applications have rapidly emerged as key drivers of business advantage. Gartner ranked "AI and Advanced Machine Learning" as of one of its 10 strategic technology initiatives for 2017, citing a wide range of potential use cases including ones in autonomous vehicles, mesh devices and virtual assistants/advisors. AI in networking is key to a future of automation, since WAN connectivity will need to keep pace if all of these AI-enabled innovations are to reach their full potential. Consider the case of chatbots: These AI-powered programs made headlines when Facebook, among other tech titans, highlighted their utility in streamlining basic online activities such as customer support and ticket purchases. AI and machine learning in networking have become more useful as WAN requirements have evolved.

Mark Johnson wants to beat the United States Department of Agriculture at its own game: predicting yields of America's crops. The USDA puts boots on the ground, deploying hundreds of workers to survey thousands of farms a month ahead of the October corn harvest, America's biggest crop. Johnson's startup, Descartes Labs, has just 20 employees, and they never leave the office in Los Alamos, New Mexico. Instead, Descartes relies on 4 petabytes of satellite imaging data and a machine learning algorithm to figure out how healthy the corn crop is from space. Corn yield prediction is big business in the US. Billions of dollars are at stake along the ag supply chain each year as corn starts to come out of the ground in August.

In an effort to stay relevant, Galaxy's Australian Futures Survey reveals proactive workers have: The future of work is a hot-button topic being tackled by the #WTFAustralia campaign, which aims to start a conversation about the big issues and encourage problem solvers to share their ideas. Readers can join in tomorrow on the What's the Future, Australia? You can ask an expert for advice if you're concerned or there's a chance to win $500 just by sharing your ideas on the issue. Social analyst David Chalke said whether new technology should be a source of worry or excitement for workers depended on their situation. "If you are 50-plus, tired, low paid and low skilled, you should be terrified because the jobs for you in the future are not going to be there, they will be automated," he said.

Russia is testing solar-powered drones that can fly for days at a time above the clouds. If the trial is successful the large glider-like drones could perform some of the same functions as today's space satellites. The model LA-252 Aist will be tested at a height of nine to 13 miles (15 - 21 kilometres) and can be used as a communication device, repeater and Wi-Fi transmitter. Russia is testing solar-powered drones that can fly for days at a time above the clouds. The large glider-like drones could perform some of the same functions as today's space satellites If the trial is successful the large glider-like drones could perform some of the same functions as today's space satellites.

Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200 - 500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging.

Lockheed Martin's interest in a San Diego start-up shows how big aerospace companies are pushing the drone revolution out to sea. Lockheed Martin Ventures last month invested an undisclosed amount in San Diego-based Ocean Aero -- a 25-employee start-up that is developing the Submaran, a solar- and wind-powered ocean drone capable of operating above and below the surface. "The ability to be environmentally powered allows us to maneuver at great persistence because it's renewable," said Eric Patten, chief executive of Ocean Aero and a former Navy officer. "And then to be able to transition that vehicle from the surface to a sub-surface vehicle that has significant capability under water, that is truly unique." Lockheed Martin Venture typically invests $1 million to $5 million in young companies.

Imagine reducing your training time for an epoch from 30 minutes to 30 seconds, and testing many different hyper-parameter weights in parallel. Available now, in public preview, Batch AI is a new service that helps you train and test deep learning and other AI or machine learning models with the same scale and flexibility used by Microsoft's data scientists. Managed clusters of GPUs enable you to design larger networks, run experiments in parallel and at scale to reduce iteration time and make development easier and more productive. Spin up a cluster when you need GPUs, then turn them off when you're done and stop the bill. Developing powerful AI involves combining large data sets for training with clusters of GPUs for experimenting with network design and optimization of hyper-parameters.